
## Import data wrangling libraries
import warnings
warnings.filterwarnings("ignore", message="Automatic reindexing on DataFrame vs Series comparisons is deprecated")
import numpy as np
import pandas as pd
from pandas import read_csv
from pandas.plotting import scatter_matrix
import random as rnd
from numpy.linalg import eig


## Import visualization libraries
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib import pyplot
from matplotlib.pyplot import MultipleLocator


## import interactive Bokeh libraries
from bokeh.io import push_notebook, show, output_notebook
from bokeh.palettes import Category20c
from bokeh.layouts import row 
from bokeh.plotting import figure
from bokeh.transform import cumsum
from bokeh.io import show
import pandas_bokeh
from bokeh.io import output_notebook
from bokeh.plotting import figure, show
from bokeh.plotting import figure, output_file, show
from bokeh.models.widgets import DataTable, TableColumn
pandas_bokeh.output_notebook()
from bokeh.plotting import output_file, show
from bokeh.colors import RGB


## import machine learning model

from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVR
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn import neighbors
from sklearn.ensemble import AdaBoostRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import GradientBoostingRegressor


from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error 
from math import sqrt
from sklearn.model_selection import RepeatedKFold
from numpy import absolute
from sklearn.decomposition import PCA
from numpy import mean
from numpy import std
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.metrics import r2_score


## Lodining data into pandas dataframe

data = pd.read_csv("data/kc_house_data.csv") # kc_data set from kaggle 
data = data.iloc[:,0:len(data)]# data retrieval
pd.set_option('display.max_columns', 25) # display 25 columns
pd.set_option('display.max_rows', 10) # display 10 rows
pd.set_option('display.width', 1000) # display 1000 charectors


data.info() # data information

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21613 entries, 0 to 21612
Data columns (total 21 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   id             21613 non-null  int64  
 1   date           21613 non-null  object 
 2   price          21613 non-null  float64
 3   bedrooms       21613 non-null  int64  
 4   bathrooms      21613 non-null  float64
 5   sqft_living    21613 non-null  int64  
 6   sqft_lot       21613 non-null  int64  
 7   floors         21613 non-null  float64
 8   waterfront     21613 non-null  int64  
 9   view           21613 non-null  int64  
 10  condition      21613 non-null  int64  
 11  grade          21613 non-null  int64  
 12  sqft_above     21613 non-null  int64  
 13  sqft_basement  21613 non-null  int64  
 14  yr_built       21613 non-null  int64  
 15  yr_renovated   21613 non-null  int64  
 16  zipcode        21613 non-null  int64  
 17  lat            21613 non-null  float64
 18  long           21613 non-null  float64
 19  sqft_living15  21613 non-null  int64  
 20  sqft_lot15     21613 non-null  int64  
dtypes: float64(5), int64(15), object(1)
memory usage: 3.5+ MB


print('Shape of the train dataset:', data.shape) ## get the shape of the train data

Shape of the train dataset: (21613, 21)


## Check for NaN values
data.isna().sum()

id               0
date             0
price            0
bedrooms         0
bathrooms        0
                ..
zipcode          0
lat              0
long             0
sqft_living15    0
sqft_lot15       0
Length: 21, dtype: int64


## Check column values
data.columns.values

array(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',
       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',
       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated',
       'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15'],
      dtype=object)


## Checking duplicate entries
duplicate = data[data.duplicated('id')] 

duplicate


## Drop duplicate entries from the dataset

data = data.drop_duplicates(subset='id')
print(data.shape)

(21436, 21)


## Checking duplicate entries
duplicate = data[data.duplicated('id')] 
duplicate.head(5)


##  get top 5 rows of dataset

data.head(5)


##  get bottom 5 rows of dataset
data.tail(5)


## Describe data
pd.set_option('display.float_format', '{:.2f}'.format) # set to display 2 decimal places.
data.describe()


## Statistics of categorical featur date
data.describe(include=['O']) # object data type


## check value counts in waterfront feature
data.groupby(['waterfront'])['waterfront'].count()

waterfront
0    21273
1      163
Name: waterfront, dtype: int64


data.groupby(['waterfront'])['price'].mean()

waterfront
0    531937.20
1   1661876.02
Name: price, dtype: float64


## Counting the number of bedrooms 
data['bedrooms'].value_counts()

3     9731
4     6849
2     2736
5     1586
6      265
      ... 
8       13
9        6
10       3
11       1
33       1
Name: bedrooms, Length: 13, dtype: int64


## Counting the number of bedrooms 
data.groupby(['bedrooms'])['price'].mean()

bedrooms
0     409503.85
1     317680.59
2     400925.49
3     466482.55
4     635648.62
        ...    
8    1105076.92
9     893999.83
10    819333.33
11    520000.00
33    640000.00
Name: price, Length: 13, dtype: float64


## check floor value count
data['floors'].value_counts()

1.00    10559
2.00     8209
1.50     1888
3.00      611
2.50      161
3.50        8
Name: floors, dtype: int64


## Counting the numbesr of view feature
data.groupby(['view'])['view'].count()

view
0    19320
1      331
2      962
3      507
4      316
Name: view, dtype: int64


## Counting the numbesr of yr-renovated feature
data.groupby(['yr_renovated'])['yr_renovated'].count()

yr_renovated
0       20526
1934        1
1940        2
1944        1
1945        3
        ...  
2011       13
2012       11
2013       37
2014       91
2015       16
Name: yr_renovated, Length: 70, dtype: int64


## transform to date format
data['date'] = pd.to_datetime(data['date'], format='%Y%m%dT%H%M%S')
data['date']

0       2014-10-13
1       2014-12-09
2       2015-02-25
3       2014-12-09
4       2015-02-18
           ...    
21608   2014-05-21
21609   2015-02-23
21610   2014-06-23
21611   2015-01-16
21612   2014-10-15
Name: date, Length: 21436, dtype: datetime64[ns]


## Create the mean price line chart of houses over the perod  in an interactive bokeh library

plt_date= data.groupby(['date'])['price'].mean() # extract price by date
labels = list(set(data['date']))

plt_date.plot_bokeh(kind="line",title ="Mean house price over the period",
                   figsize =(1000,600), 
                   xlabel = "Date",
                   ylabel="Price", stacked = True)


## Plot histogram of numerical variables
data.hist(figsize=(16,16))
plt.show()


## Chart of waterfront vs price in bokeh library

plt_w = data.groupby(['waterfront'])['price'].mean()# data extraction, price by waterfront class

## Assign RGB color
red = 200
green = 150
blue = 100
color = RGB(r = red,
            g = green,
            b = blue)

plt_w.plot_bokeh(kind="bar", title="Mean house price group by waterfront class",
                 figsize=(800, 500),
                 xlabel="Waterfront",
                 ylabel="Price",
                 #stacked=True,
                color = color)


## Chart of price with number of bedrooms and waterfront in bokeh intractive library.

plt_p = data.groupby(['bedrooms','waterfront'])['price'].mean().unstack() # data extraction, bedrooms and waterfront
plt_p.plot_bokeh(kind="bar", title="Mean house price group by number of bedrooms and waterfront",
                 figsize=(800, 500),
                 xlabel="Bedrooms",
                 ylabel="Price",
                 stacked=False)


## Chart of floor vs price in bokeh library

plt_f = data.groupby(['floors'])['price'].mean() # extraxt floor and price details

##  Assogn RGB color
red = 300
green = 100
blue = 100
color = RGB(r = red,
            g = green,
            b = blue)

plt_f.plot_bokeh(kind="line", title="Mean house price base on floors",
                 figsize=(800, 500),
                 xlabel="Floors",
                 ylabel="Price",
                 stacked=True,
                color = color)


## Chart of floor and waterfront vs price in bokeh library

plt_fw = data.groupby(['floors','waterfront'])['price'].mean().unstack() # extraxt floor and price wiath waterfront details

plt_fw.plot_bokeh(kind="bar", title="Mean house price of floors with waterfront",
                 figsize=(800, 500),
                 xlabel="Floors",
                 ylabel="Price",
                 stacked=True,)


## Chart of bathrooms vs price in bokeh library

plt_b = data.groupby(['bathrooms'])['price'].mean() # data extraction, price by bathrooms

## Assign RGB color
red = 200
green = 100
blue = 120
color = RGB(r = red,
            g = green,
            b = blue)

plt_b.plot_bokeh(kind="bar", title="Mean house price base on bathrooms",
                 figsize=(800, 500),
                 xlabel="Bathrooms",
                 ylabel="Price",
                 stacked=True,
                color = color)


## Line chart of sqft_living vs price in bokeh library

plt_s = data.groupby(['sqft_living',])['price'].mean()# data extraction, price by bathrooms

## Assign RGB color
red = 100
green = 200
blue = 180
color = RGB(r = red,
            g = green,
            b = blue)

plt_s.plot_bokeh(kind="line", title="Mean house price base on sqft_living",
                 figsize=(800, 500),
                 xlabel="Sqft_living",
                 ylabel="Price",
                 #stacked=True,
                color = color)


## hbar chart of grade and bedrooms vs price in bokeh library

plt_g = data.groupby(['grade','bedrooms'])['price'].mean().unstack()# data extraction, price by grade and bedrooms

plt_g.plot_bokeh(kind="barh", title="Mean house price base on grade and bedrooms",
                 figsize=(800, 500),
                 xlabel="Price",
                 ylabel="Grade",
                 stacked=False,)


## point chart of lat and waterfront vs price in bokeh library

plt_g = data.groupby(['lat','waterfront'])['price'].mean().unstack()# data extraction, price by lat and waterfront

plt_g.plot_bokeh(kind="point", title="Mean house price base on lat and waterfront",
                 figsize=(800, 500),
                 xlabel="Lat",
                 ylabel="Price",
                 stacked=False,)


## Correlation matrix between features in table form
cormat = data.corr()#correlation matrix
round(cormat,2)
cormat.head(20)


## To view correlations with heatmap

plt.figure(figsize=(15, 12))
sns.heatmap(data.corr(method = 'pearson'), 
            annot = True, fmt = '.2f',
            linewidths = 0.1,
            linecolor= 'white',
             cmap='coolwarm',
            annot_kws={'size':8})
plt.title('Correlation Map Between Variables', fontsize = 15)
plt.show()


## Correlation of features with respect to the target price

df1 = cormat['price'] # target variable
df1_sorted = df1.sort_values() # sort the values

df1_sorted.plot(kind='barh',
                title='Pearson correlation coefficients between variables and price',
                color='red',
                figsize=(8, 8),
                xticks=(-1, -0.5, -0.2, -0.1, 0, 0.1, 0.2, 0.5, 1)) # range of x-values -1 to 1

<AxesSubplot:title={'center':'Pearson correlation coefficients between variables and price'}>


## A closer look at the scatter plot with price

plt.figure(figsize=(20, 10))

plt.subplot(331)
sns.scatterplot(x="sqft_living",y="price",hue='waterfront', data=data, color='green')

plt.subplot(332)
sns.scatterplot(x="grade", y="price", data=data, color='blue')

plt.subplot(333)
sns.scatterplot(x="sqft_above",y="price",hue='waterfront',  data=data, color='purple')

plt.subplot(334)
sns.scatterplot(x="sqft_living15",y="price",hue='waterfront',  data=data, color='orange')

plt.subplot(335)
sns.scatterplot(x="bathrooms", y="price",hue='waterfront', data=data, color='yellow')

plt.subplot(336)
sns.scatterplot(x="view", y="price",hue='waterfront', data=data, color='magenta')

plt.subplot(337)
sns.scatterplot(x="sqft_basement",y="price",hue='waterfront',  data=data, color='red')

plt.subplot(338)
sns.scatterplot(x="bedrooms",y="price",hue='waterfront',  data=data, color='tan')

plt.subplot(339)
sns.scatterplot(x="lat",y="price",hue='waterfront',  data=data, color='lightgreen')
plt.show()


## Check data type of class feature waterfront
data['waterfront'].dtype
print(data['waterfront'].dtype)

## Make waterfront feature as object since it has 2 classess
#data['waterfront'] = data['waterfront'].astype('object')


#data['waterfront'].dtype

int64


## Check data type of class feature view
#data['view'].dtype
#print(data['view'].dtype)

## Make waterfront feature as object since it has 4 classess
#data['view'] = data['view'].astype('object')

#data['view'].dtype


## To mitigate multicollinearity, features that exhibit high correlation are dropped.
#data.drop(['zipcode', 'id', 'long', 'condition', 'yr_built', 'sqft_lot15', 'sqft_lot','yr_renovated', 'date'],axis=1, inplace =True)

## Drop the id and data
data.drop(['id', 'date'],axis=1, inplace =True)
data.shape

(21436, 19)


## detecting outliers on upper and lower quantiles using the functions

def quantile_up(data1):
  
    Q1 = data1.quantile(0.25) #1st quantile - 25%
    Q3 = data1.quantile(0.75) #3rd quantile - 75%
    IQR = Q3 - Q1 #Interquantile range - IQR
    outlier_step = 1.5 * IQR
    upper_whisker = data1[data1<=Q3+1.5*IQR].max()
    return (upper_whisker)

def quantile_down(data2):
    
    Q1 = data2.quantile(0.25) #1st quantile - 25%
    Q3 = data2.quantile(0.75) #3rd quantile - 75%
    IQR = Q3 - Q1 #Interquantile range - IQR
    outlier_step = 1.5 * IQR
    lower_whisker = data2[data2>=Q1-1.5*IQR].min()
    return (lower_whisker)


## Box plot to illustrate the outliers with IQR cutoff points

plt.figure(figsize=(16,14))
labels = list(set(data.columns)) # list of unique column from dataset
for x in range(19): # iterate over the range 0-18
    plt.subplot(5,4,x+1) # set plot 5 rows and 4 rows
    plt.boxplot(data.iloc[:,x], 
                whis = 1.5, 
                widths = 0.6, 
                flierprops = {'marker':'D', 
                              'markerfacecolor':'red', 
                              'markeredgecolor':'red', 
                              'markersize':3})
    
    plt.axhline(y = quantile_up(data.iloc[:,x]), color='b', linestyle='--')# draw horizontal dashed line upper border
    plt.axhline(y = quantile_down(data.iloc[:,x]), color='b', linestyle='--')# draw horizontal dashed line lower border
    plt.title(data.columns[x])
plt.show()


## Ploting the remaining data after removing the outliers

dfr = data.copy()
Q1 = dfr.quantile(0.25)#1st quantile - 25%
Q3 = dfr.quantile(0.75)#3rd quantile - 75%
IQR = Q3 - Q1 #Interquantile range - IQR
df_rem = dfr[~((dfr < (Q1 - 1.5 * IQR)) | \
               (dfr> (Q3 + 1.5 * IQR))).any(axis=1)]

print('Remaining data:',df_rem.shape)
df_rem.info()

# plot box and whisker plots after clearing outliers
fig, axes = plt.subplots(5, 4, figsize=(14, 14)) # 5 rows with 4 columns
axes = axes.flatten() # create an array to iterate 

for i, col in enumerate(df_rem.columns): # to iterate over the colums 
    sns.boxplot(x=df_rem[col], ax=axes[i])# plot boxplot 
    axes[i].set_title(col) # label

plt.tight_layout()
plt.show()

Remaining data: (14813, 19)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 14813 entries, 0 to 21612
Data columns (total 19 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   price          14813 non-null  float64
 1   bedrooms       14813 non-null  int64  
 2   bathrooms      14813 non-null  float64
 3   sqft_living    14813 non-null  int64  
 4   sqft_lot       14813 non-null  int64  
 5   floors         14813 non-null  float64
 6   waterfront     14813 non-null  int64  
 7   view           14813 non-null  int64  
 8   condition      14813 non-null  int64  
 9   grade          14813 non-null  int64  
 10  sqft_above     14813 non-null  int64  
 11  sqft_basement  14813 non-null  int64  
 12  yr_built       14813 non-null  int64  
 13  yr_renovated   14813 non-null  int64  
 14  zipcode        14813 non-null  int64  
 15  lat            14813 non-null  float64
 16  long           14813 non-null  float64
 17  sqft_living15  14813 non-null  int64  
 18  sqft_lot15     14813 non-null  int64  
dtypes: float64(5), int64(14)
memory usage: 2.3 MB


## Plot histogram before and after clearing outliers
plt.figure(figsize=(15,20))
for x in range(10): # iterate over range 0-18
    plt.subplot(3,4,x+1) # 3 rows and 4 columns
    data.iloc[:,x].hist(alpha = 0.7) # plot histogram of the values of x-column -before
    df_rem.iloc[:,x].hist(alpha = 0.5)# # plot histogram of the values of x-column - after
    plt.legend(['Before', 'After'])
    plt.title(df_rem.columns[x])
plt.show()


## Check the remained data as data for further analysis. 
data = df_rem
data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 14813 entries, 0 to 21612
Data columns (total 19 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   price          14813 non-null  float64
 1   bedrooms       14813 non-null  int64  
 2   bathrooms      14813 non-null  float64
 3   sqft_living    14813 non-null  int64  
 4   sqft_lot       14813 non-null  int64  
 5   floors         14813 non-null  float64
 6   waterfront     14813 non-null  int64  
 7   view           14813 non-null  int64  
 8   condition      14813 non-null  int64  
 9   grade          14813 non-null  int64  
 10  sqft_above     14813 non-null  int64  
 11  sqft_basement  14813 non-null  int64  
 12  yr_built       14813 non-null  int64  
 13  yr_renovated   14813 non-null  int64  
 14  zipcode        14813 non-null  int64  
 15  lat            14813 non-null  float64
 16  long           14813 non-null  float64
 17  sqft_living15  14813 non-null  int64  
 18  sqft_lot15     14813 non-null  int64  
dtypes: float64(5), int64(14)
memory usage: 2.3 MB


## Set the target variable as price

target = data['price']
target.shape

(14813,)


## Drop price from the data
data = data.copy() # created a copy of the data
data.drop('price', axis=1, inplace=True) # drop the price varaible
print(data.shape)

(14813, 18)


## Stadard Scaling of data
from sklearn.preprocessing import StandardScaler

#features = list(data.select_dtypes(exclude=['object']).columns) # select data type (numeric)
features = list(data.select_dtypes(include=['int64', 'float64', 'int32']).columns) # select data type (numeric)
data_scaler = StandardScaler() # assign scaler instance
data_scaled = pd.DataFrame(data = data) # copy and name the new dataframe
data_scaled[features] = data_scaler.fit_transform(data_scaled[features]) # sacle the features and transformed

print(np.mean(data), np.std(data))

bedrooms        -0.00
bathrooms        0.00
sqft_living      0.00
sqft_lot        -0.00
floors          -0.00
                 ... 
zipcode         -0.00
lat              0.00
long             0.00
sqft_living15    0.00
sqft_lot15       0.00
Length: 18, dtype: float64 bedrooms        1.00
bathrooms       1.00
sqft_living     1.00
sqft_lot        1.00
floors          1.00
                ... 
zipcode         1.00
lat             1.00
long            1.00
sqft_living15   1.00
sqft_lot15      1.00
Length: 18, dtype: float64


data.head()# view scaled data head


## Droped features with sigle zero values
data.drop(['waterfront','view','yr_renovated'],axis = 1, inplace =True)


## Review the correlation heatmap after stadardization

plt.figure(figsize=(15, 12))
#color = plt.get_cmap('RdYlGn')   # default color
#color.set_bad('lightblue')
sns.heatmap(data.corr(method = 'pearson'), 
            annot = True, fmt = '.2f',
            linewidths = 0.1,
            linecolor= 'white',
            #vmin=0.5, 
            #vmax=0.7,
            annot_kws={'size':8})
plt.title('Correlation heatmap after standardization', fontsize = 15)
plt.show()


data.shape # check the data shape

(14813, 15)


# Check column name values
data.columns

Index(['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15'], dtype='object')


## Import libraries
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_regression

## Assigning X as predictors and y as target as usual
X = data
y = target

selector = SelectKBest(score_func=f_regression, k='all') # f_regression to get feature scores
selector.fit(X, y) # fitting with selector

f_scores = selector.scores_ # get featur scores
f_names = X.columns # get featur names
f_scores_df = pd.DataFrame({'Feature': f_names, 'Score': f_scores}) # dataframe to store feature name and score
f_scores_df = f_scores_df.sort_values(by='Score', ascending=False)# sort the feature score 

df3 = f_scores_df.loc[:, ['Feature', 'Score']] # best feature and score dataframe
df3 # view


## Plot the best features with higher score

labels = list(set(df3['Feature']))
fig = plt.figure(figsize=(15, 6))
ax = fig.add_subplot(111)
df3.plot.bar(x='Feature', y='Score', ax=ax)

#plt.show()

<AxesSubplot:xlabel='Feature'>


## Select the top features based on score
#from sklearn.feature_selection import SelectKBest, f_regression

#selector = SelectKBest(score_func=f_regression, k=13) # Select K Best feature selector
#X_selected = selector.fit_transform(X, y)  # Fit the selector 
#features_selected = X.columns[selector.get_support()] # selected features
#X_selected = pd.DataFrame(X_selected, columns=features_selected) # new dataframe with selected features

#X_selected # Use X_selected for further analysis and modeling


#X_selected.columns # look the column name valus


#from sklearn.linear_model import Lasso
#from sklearn.model_selection import GridSearchCV

#lasso = Lasso()

# Define the alpha values to be searched
#param_grid = {'alpha': [0.01, 0.1, 1.0, 10.0]}
#param_grid = {'alpha': np.linspace(0.1, 1, 100)}

# Perform grid search with cross-validation
#grid_search = GridSearchCV(lasso, param_grid, cv=5)
#grid_search.fit(X, y)

#best_alpha = grid_search.best_params_['alpha']
#print("Best alpha:", best_alpha)


## feature selection 
#import libraries

from sklearn.linear_model import Lasso
from sklearn.feature_selection import SelectFromModel

X = data
y = target

lasso = Lasso(alpha=1000)  # set the alpha value as per your requirement
lasso.fit(X, y)

my_feature = SelectFromModel(lasso, prefit=True) # lasso feature selection 
X_selected = my_feature.transform(X) # selected features based on lasso co efficients

feature_id = my_feature.get_support(indices=True) # get the selected feature names 
selected_f = X.columns[feature_id]

X_selected = X[selected_f].copy()
#X_selected

#print("Selected Features:")
#print(selected_f)

c:\users\h207171\appdata\local\programs\python\python39\lib\site-packages\sklearn\base.py:432: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(


# Check the inportance of each feature
import catboost as cb
cb = cb.CatBoostRegressor()
cb.fit(data,target)

importances = pd.DataFrame({'feature':data.columns,'importance':np.round(cb.feature_importances_,3)})
importances = importances.sort_values('importance',ascending=False).set_index('feature')
importances.head(17)

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## Importances.plot.bar()
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111)

importances.plot.bar(ax=ax)

plt.show()


data1= data[['sqft_living', 'sqft_lot','condition', 'grade', 'sqft_above',
            'yr_built', 'lat', 'long', 'sqft_living15', 'sqft_lot15','sqft_basement','zipcode']]


# Check the inportance of each feature
import catboost as cb
cb = cb.CatBoostRegressor()
cb.fit(data1,target)

importances = pd.DataFrame({'feature':data1.columns,'importance':np.round(cb.feature_importances_,3)})
importances = importances.sort_values('importance',ascending=False).set_index('feature')
importances.head(17)

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## Importances.plot.bar()
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111)

importances.plot.bar(ax=ax)

plt.show()


## Preparing data for traing and testing by spliting

X = data1.values # get predictor variable values
y = target.values # get target variable values

## spliting dataset to 80/20 proportion for training and testing
X_train,X_test, y_train, y_test = train_test_split(X,y, test_size=0.2,random_state=42)


## check the shape of the split
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)

(11850, 12)
(11850,)
(2963, 12)
(2963,)


from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score, KFold
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings("ignore", category=ConvergenceWarning)

lr_models =[]

lr_models.append(('Linear Regression',LinearRegression()))
lr_models.append(('Ridge',Ridge(alpha=0.1)))
lr_models.append(('Lasso',Lasso(alpha=0.8,max_iter=1000)))

## Evaluate each model in turn
results = []
names = []

for name, model in lr_models:
    fit = model.fit(X_train, y_train)
    y_pred = model.predict(X_test)

    #print(name, "Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[0]), np.round(y_test[0])))
    #print(name, "Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[1]), np.round(y_test[1])))
    
    r_squared_train = model.score(X_train, y_train) # compute R-sdquared value for train
    r_squared_test = model.score(X_test, y_test) # compute R-sdquared value for test
    rmse = mean_squared_error(y_test, y_pred, squared=False) # compute root mean square

    # Print the metrics
    print(name,"Train_R^2: {:.2f}%".format(r_squared_train * 100))
    print(name,"Test_R^2: {:.2f}%".format(r_squared_test * 100))
    print(name,"RMSE: {:.2f}".format(rmse))

## Cross validation
    kf = KFold(n_splits=6, shuffle=True, random_state=5) # 6 fold cross validation
    cv_scores = cross_val_score(model,X_train, y_train, cv=kf) # Compute 6-fold cross-validation scores

    # Print scores
    print(name,"Mean cv Score: {:.2f}%".format(np.mean(cv_scores) * 100)) # mean cv score
    print(name,"Standard Deviation of CV Scores: {:.3f}".format(np.std(cv_scores))) #mean std-dev score
    #print('slope:', np.round(model.coef_,2))
    #print('intercept:',np.round(model.intercept_,2))
    print()

Linear Regression Train_R^2: 65.23%
Linear Regression Test_R^2: 65.43%
Linear Regression RMSE: 106612.06
Linear Regression Mean cv Score: 65.02%
Linear Regression Standard Deviation of CV Scores: 0.011

Ridge Train_R^2: 65.23%
Ridge Test_R^2: 65.43%
Ridge RMSE: 106612.32
Ridge Mean cv Score: 65.03%
Ridge Standard Deviation of CV Scores: 0.011

Lasso Train_R^2: 65.23%
Lasso Test_R^2: 65.43%
Lasso RMSE: 106612.27
Lasso Mean cv Score: 65.03%
Lasso Standard Deviation of CV Scores: 0.011


## 2 - Model KNN
model2 = KNeighborsRegressor(weights='uniform')
model2.fit(X_train,y_train)

y_pred = model2.predict(X_test)

print("KNN_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[0]), np.round(y_test[0])))
print("KNN_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[1]), np.round(y_test[1])))

r_squared_train_knn = model2.score(X_train, y_train) # compute R-sdquared value for train
r_squared_test_knn = model2.score(X_test, y_test) # compute R-sdquared value for test
rmse_knn = mean_squared_error(y_test, y_pred, squared=False) # compute root mean square

# Print the metrics
print("Train_R^2: {:.2f}%".format(r_squared_train_knn * 100))
print("Test_R^2: {:.2f}%".format(r_squared_test_knn * 100))
print("RMSE: {:.2f}".format(rmse_knn))

## Cross validation
kf = KFold(n_splits=6, shuffle=True, random_state=5) # 6 fold cross validation
cv_scores = cross_val_score(model2,X_train, y_train, cv=kf) # Compute 6-fold cross-validation scores

# Print scores
print("Mean cv Score: {:.2f}%".format(np.mean(cv_scores) * 100)) # mean cv score
print("Standard Deviation of CV Scores: {:.3f}".format(np.std(cv_scores))) #mean std-dev score
print()

KNN_Predictions: 416950, Actual Values: 431000
KNN_Predictions: 326202, Actual Values: 240000
Train_R^2: 86.14%
Test_R^2: 78.71%
RMSE: 83653.87
Mean cv Score: 78.11%
Standard Deviation of CV Scores: 0.014


## plot the test, predicted score KNN
plt.figure(figsize=(15,10))
#sns.regplot(x=y_test, y= y_pred)
sns.regplot(x=y_test, y=y_pred, scatter_kws={'color': 'pink'}, line_kws={'color': 'green'})
plt.title('Test vs Predicted Scores')
plt.xlabel('Original Price')
plt.ylabel('Predicted Price')
plt.show()


## 3 - Model Decision tree
model3 = DecisionTreeRegressor()
model3.fit(X_train,y_train)

y_pred = model3.predict(X_test)

print("TREE_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[0]), np.round(y_test[0])))
print("TREE_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[1]), np.round(y_test[1])))

r_squared_train_tree = model3.score(X_train, y_train) # compute R-sdquared value for train
r_squared_test_tree = model3.score(X_test, y_test) # compute R-sdquared value for test
rmse_tree = mean_squared_error(y_test, y_pred, squared=False) # compute root mean square

# Print the metrics
print("Train_R^2: {:.2f}%".format(r_squared_train_tree * 100))
print("Test_R^2: {:.2f}%".format(r_squared_test_tree * 100))
print("RMSE: {:.2f}".format(rmse_tree))

## Cross validation
kf = KFold(n_splits=6, shuffle=True, random_state=5) # 6 fold cross validation
cv_scores = cross_val_score(model3,X_train, y_train, cv=kf) # Compute 6-fold cross-validation scores

# Print scores
print("Mean cv Score: {:.2f}%".format(np.mean(cv_scores) * 100)) # mean cv score
print("Standard Deviation of CV Scores: {:.3f}".format(np.std(cv_scores))) #mean std-dev score
print()

TREE_Predictions: 331000, Actual Values: 431000
TREE_Predictions: 265000, Actual Values: 240000
Train_R^2: 100.00%
Test_R^2: 76.07%
RMSE: 88687.56
Mean cv Score: 73.72%
Standard Deviation of CV Scores: 0.015


## plot the test, predicted score TREE
plt.figure(figsize=(15,10))
#sns.regplot(x=y_test, y= y_pred)
sns.regplot(x=y_test, y=y_pred, scatter_kws={'color': 'purple'}, line_kws={'color': 'green'})
plt.title('Test vs Predicted Scores')
plt.xlabel('Original Price')
plt.ylabel('Predicted Price')
plt.show()


## 4 - Model Random Forest
model4 = RandomForestRegressor()
model4.fit(X_train,y_train)

y_pred = model4.predict(X_test)

print("RF_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[0]), np.round(y_test[0])))
print("RF_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[1]), np.round(y_test[1])))

r_squared_train_rf = model4.score(X_train, y_train) # compute R-sdquared value for train
r_squared_test_rf = model4.score(X_test, y_test) # compute R-sdquared value for test
rmse_rf = mean_squared_error(y_test, y_pred, squared=False) # compute root mean square
mae_rf = mean_absolute_error(y_test, y_pred)
mse_rf = mean_squared_error(y_test, y_pred)

# Print the metrics
print("Train_R^2: {:.2f}%".format(r_squared_train_rf * 100))
print("Test_R^2: {:.2f}%".format(r_squared_test_rf * 100))
print("RMSE(RF): {:.2f}".format(rmse_rf))
print('MAE (RF): {:.2f}'.format(mae_rf))
print('MSE (RF): {:.2f}'.format(mse_rf))


## Cross validation
kf = KFold(n_splits=6, shuffle=True, random_state=5) # 6 fold cross validation
cv_scores = cross_val_score(model4,X_train, y_train, cv=kf) # Compute 6-fold cross-validation scores

# Print scores
print("Mean cv Score: {:.2f}%".format(np.mean(cv_scores) * 100)) # mean cv score
print("Standard Deviation of CV Scores: {:.3f}".format(np.std(cv_scores))) #mean std-dev score
print()

RF_Predictions: 323589, Actual Values: 431000
RF_Predictions: 322388, Actual Values: 240000
Train_R^2: 98.19%
Test_R^2: 87.96%
RMSE(RF): 62901.19
MAE (RF): 44275.77
MSE (RF): 3956559852.99
Mean cv Score: 86.65%
Standard Deviation of CV Scores: 0.008


## plot the test, predicted score RF
plt.figure(figsize=(15,10))
#sns.regplot(x=y_test, y= y_pred)
sns.regplot(x=y_test, y=y_pred, scatter_kws={'color': 'orange'}, line_kws={'color': 'red'})
plt.title('Test vs Predicted Scores')
plt.xlabel('Original Price')
plt.ylabel('Predicted Price')
plt.show()


from xgboost import XGBRegressor

## 5 - Model XGB
#model5 = XGBRegressor()

model5 = XGBRegressor(subsample= 0.9,
                      n_estimators=1500,
                      max_depth = 5,
                      learning_rate = 0.1,
                      colsample_bytree = 0.6
                     )
model5.fit(X_train,y_train)

y_pred = model5.predict(X_test)

print("XGB_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[0]), np.round(y_test[0])))
print("XGB_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[1]), np.round(y_test[1])))

r_squared_train_xgb = model5.score(X_train, y_train) # compute R-sdquared value for train
r_squared_test_xgb = model5.score(X_test, y_test) # compute R-sdquared value for test
rmse_xgb = mean_squared_error(y_test, y_pred, squared=False) # compute root mean square
mae_xgb = mean_absolute_error(y_test, y_pred)
mse_xgb = mean_squared_error(y_test, y_pred)

# Print the metrics
print("Train_R^2: {:.2f}%".format(r_squared_train_xgb * 100))
print("Test_R^2: {:.2f}%".format(r_squared_test_xgb * 100))
print("RMSE (XGB): {:.2f}".format(rmse_xgb))
print('MAE (XGB): {:.2f}'.format(mae_xgb))
print('MSE (XGB): {:.2f}'.format(mse_xgb))

## Cross validation
kf = KFold(n_splits=6, shuffle=True, random_state=5) # 6 fold cross validation
cv_scores = cross_val_score(model5,X_train, y_train, cv=kf) # Compute 6-fold cross-validation scores

# Print scores
print("Mean cv Score: {:.2f}%".format(np.mean(cv_scores) * 100)) # mean cv score
print("Standard Deviation of CV Scores: {:.3f}".format(np.std(cv_scores))) #mean std-dev score
print()

XGB_Predictions: 340396, Actual Values: 431000
XGB_Predictions: 283436, Actual Values: 240000
Train_R^2: 98.74%
Test_R^2: 89.15%
RMSE (XGB): 59727.61
MAE (XGB): 42336.54
MSE (XGB): 3567387169.54
Mean cv Score: 87.81%
Standard Deviation of CV Scores: 0.011


## plot the test, predicted score XGB
plt.figure(figsize=(15,10))
#sns.regplot(x=y_test, y= y_pred)
sns.regplot(x=y_test, y=y_pred, scatter_kws={'color': 'magenta'}, line_kws={'color': 'green'})
plt.title('Test vs Predicted Scores')
plt.xlabel('Original Price')
plt.ylabel('Predicted Price')
plt.show()


## 6 - Model GBR
#model6 = GradientBoostingRegressor()

model6 = GradientBoostingRegressor(subsample=0.8,
                                   n_estimators=1500,
                                   max_depth=7,
                                   learning_rate=0.01,
                                   #colsample_bytree=0.6
                                  )
model6.fit(X_train,y_train)

y_pred = model6.predict(X_test)

print("GBR_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[0]), np.round(y_test[0])))
print("GBR_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[1]), np.round(y_test[1])))

r_squared_train_gbr = model6.score(X_train, y_train) # compute R-sdquared value for train
r_squared_test_gbr = model6.score(X_test, y_test) # compute R-sdquared value for test
rmse_gbr = mean_squared_error(y_test, y_pred, squared=False) # compute root mean square
mae_gbr = mean_absolute_error(y_test, y_pred)
mse_gbr = mean_squared_error(y_test, y_pred)

# Print the metrics
print("Train_R^2: {:.2f}%".format(r_squared_train_gbr * 100))
print("Test_R^2: {:.2f}%".format(r_squared_test_gbr * 100))
print("RMSE (GBR): {:.2f}".format(rmse_gbr))
print('MAE (GBR): {:.2f}'.format(mae_gbr))
print('MSE (GBR): {:.2f}'.format(mse_gbr))

## Cross validation
#kf = KFold(n_splits=6, shuffle=True, random_state=5) # 6 fold cross validation
#cv_scores = cross_val_score(model6,X_train, y_train, cv=kf) # Compute 6-fold cross-validation scores

# Print scores
#print("Mean cv Score: {:.2f}%".format(np.mean(cv_scores) * 100)) # mean cv score
#print("Standard Deviation of CV Scores: {:.3f}".format(np.std(cv_scores))) #mean std-dev score
#print()

GBR_Predictions: 368807, Actual Values: 431000
GBR_Predictions: 282150, Actual Values: 240000
Train_R^2: 96.41%
Test_R^2: 89.38%
RMSE (GBR): 59090.50
MAE (GBR): 41786.17
MSE (GBR): 3491686944.00


## plot the test, predicted score GBR
plt.figure(figsize=(15,10))
#sns.regplot(x=y_test, y= y_pred)
sns.regplot(x=y_test, y=y_pred, scatter_kws={'color': 'green'}, line_kws={'color': 'red'})
plt.title('Test vs Predicted Scores')
plt.xlabel('Original Price')
plt.ylabel('Predicted Price')
plt.show()


## Moedl 7 CBR
import catboost as cb
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error

model7 = cb.CatBoostRegressor()
model7.fit(X_train,y_train,verbose=False)

y_pred = model7.predict(X_test)

print("CBR_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[0]), np.round(y_test[0])))
print("CBR_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[1]), np.round(y_test[1])))

r_squared_train_cbr = model7.score(X_train, y_train) # compute R-sdquared value for train
r_squared_test_cbr = model7.score(X_test, y_test) # compute R-sdquared value for test
rmse_cb = mean_squared_error(y_test, y_pred, squared=False) # compute root mean square
mae_cb = mean_absolute_error(y_test, y_pred)
mse_cb = mean_squared_error(y_test, y_pred)

# Print the metrics
print("Train_R^2: {:.2f}%".format(r_squared_train_cbr * 100))
print("Test_R^2: {:.2f}%".format(r_squared_test_cbr * 100))
print("RMSE (CBR): {:.2f}".format(rmse_cb))
print('MAE (CBR): {:.2f}'.format(mae_cb))
print('MSE (CBR): {:.2f}'.format(mse_cb))

CBR_Predictions: 387948, Actual Values: 431000
CBR_Predictions: 288067, Actual Values: 240000
Train_R^2: 93.38%
Test_R^2: 89.69%
RMSE (CBR): 58215.37
MAE (CBR): 41334.25
MSE (CBR): 3389028823.09


## Cross validation
#model7.fit(X_train,y_train,verbose=False)
#kf = KFold(n_splits=6, shuffle=True, random_state=5) # 6 fold cross validation
#cv_scores = cross_val_score(model7, cv=kf) # Compute 6-fold cross-validation scores

model7.fit(X_train, y_train, verbose=False)
kf = KFold(n_splits=6, shuffle=True, random_state=5) # 6-fold cross validation
cv_scores = cross_val_score(model7, X_train, y_train, cv=kf, verbose = 0) # Compute 6-fold cross-validation scores


# Print scores
print("Mean cv Score: {:.2f}%".format(np.mean(cv_scores) * 100)) # mean cv score
print("Standard Deviation of CV Scores: {:.3f}".format(np.std(cv_scores))) #mean std-dev score
#print()

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Mean cv Score: 88.55%
Standard Deviation of CV Scores: 0.008


## plot the test, predicted score CBR
plt.figure(figsize=(15,10))
#sns.regplot(x=y_test, y= y_pred)
sns.regplot(x=y_test, y=y_pred, scatter_kws={'color': 'tan'}, line_kws={'color': 'green'})
plt.title('Test vs Predicted Scores')
plt.xlabel('Original Price')
plt.ylabel('Predicted Price')
plt.show()


results = pd.DataFrame({
    'Model': ['KNN', 'TREE', 'Random Forest', 
              'XGB', 'GBR', 'CBR'],
 
    'Score': [r_squared_test_knn,
              r_squared_test_tree,
              r_squared_test_rf,
              r_squared_test_xgb,
              r_squared_test_gbr,
              r_squared_test_cbr],
    'RMSE':[rmse_knn,
           rmse_tree,
           rmse_rf,
           rmse_xgb,
           rmse_gbr,
           rmse_cb]
             })
result_df = results.sort_values(by='Score', ascending=False)
result_df = result_df.set_index('Score')
result_df.head(9)


from sklearn.model_selection import GridSearchCV
from catboost import CatBoostRegressor
import numpy as np

# Define the parameter grid

param_grid = {
    "learning_rate": [0.03, 0.04, 0.06],
    "iterations": np.linspace(100, 3000, 2, dtype=int),
    "depth": [5, 8, 10],
    "l2_leaf_reg": [2, 3, 5],
}

# Create a CatBoost regressor
catboost = CatBoostRegressor()

# Initialize GridSearchCV
clf = GridSearchCV(estimator=catboost, param_grid=param_grid, cv=5, verbose= False, n_jobs=-1)

# Fit GridSearchCV on the training data
#clf.fit(X_train, y_train)

# Print the best parameters found
#print(clf.best_params_)


## Train the cat boose model with best parameters
## 7 - Model XB
#import catboost as cb
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error


model7=cb.CatBoostRegressor(iterations=2000, 
                     learning_rate=0.03, 
                     depth=8, 
                     l2_leaf_reg=2, 
                     loss_function='RMSE', 
                     verbose=True)             

model7.fit(X_train,y_train,verbose=False)

y_pred = model7.predict(X_test)

print("CBR_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[0]), np.round(y_test[0])))
print("CBR_Predictions: {:.0f}, Actual Values: {:.0f}".format(np.round(y_pred[1]), np.round(y_test[1])))

r_squared_train_cb = model7.score(X_train, y_train) # compute R-sdquared value for train
r_squared_test_cb = model7.score(X_test, y_test) # compute R-sdquared value for test
rmse_cb = mean_squared_error(y_test, y_pred, squared=False) # compute root mean square
mae_cb = mean_absolute_error(y_test, y_pred)
mse_cb = mean_squared_error(y_test, y_pred)

# Print the metrics
print("Train_R^2: {:.2f}%".format(r_squared_train_cb * 100))
print("Test_R^2: {:.2f}%".format(r_squared_test_cb * 100))
print("RMSE (CBR): {:.2f}".format(rmse_cb))
print('MAE (CBR): {:.2f}'.format(mae_cb))
print('MSE (CBR): {:.2f}'.format(mse_cb))

CBR_Predictions: 363463, Actual Values: 431000
CBR_Predictions: 284051, Actual Values: 240000
Train_R^2: 95.97%
Test_R^2: 89.90%
RMSE (CBR): 57628.23
MAE (CBR): 40742.03
MSE (CBR): 3321013142.04


## Cross validation after fine tune
  
kf = KFold(n_splits=10,shuffle=True, random_state= 5) # 6 fold cross validation
cv_scores = cross_val_score(model7,X_train, y_train, cv=kf) # Compute 6-fold cross-validation scores

# Print scores

print("Mean cv Score: {:.2f}%".format(np.mean(cv_scores) * 100)) # mean cv score
print("Standard Deviation of CV Scores: {:.3f}".format(np.std(cv_scores))) #mean std-dev score
#print()

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Mean cv Score: 88.79%
Standard Deviation of CV Scores: 0.011


## plot the test, predicted score
plt.figure(figsize=(15,10))
#sns.regplot(x=y_test, y= y_pred)
sns.regplot(x=y_test, y=y_pred, scatter_kws={'color': 'blue'}, line_kws={'color': 'red'})
plt.title('Test vs Predicted Scores')
plt.xlabel('Original Price')
plt.ylabel('Predicted Price')
plt.show()


## Plot the loss function with cat boost regressor

model7.fit(X_train, y_train, verbose=False)

# Generate the loss function plot
plt.figure(figsize=(15, 10))
iterations = range(1, len(model7.get_feature_importance()) + 1)
losses = model7.get_feature_importance()
plt.plot(iterations, losses, label='Loss',color = 'green')
plt.xlabel('Feature')
plt.ylabel('Loss')
plt.title('CatBoost Loss Function')
plt.legend()
plt.show()


from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error

model7.fit(X_train,y_train,verbose=False)
yhat = model7.predict(X_test)
y_test = np.array(list(y_test))
yhat = np.array(list(yhat))

linear_score = r2_score(y_test, yhat)

df1 = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': yhat.flatten()})
df1 = df1.round(2)
print(df1.head(20))


## Create bar graph atual(y) vs predicted (y)
df2 = df1.head(15)
df2.plot(kind='bar',figsize=(14,7))
plt = pyplot
plt.title('Actual vs Predicted values ') 
plt.xlabel('Number of predictions')
plt.ylabel('Price (million)')
plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')
plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
plt.show()

print('Test vs Pred MAE:', round(mean_absolute_error(y_test, yhat), 2))
print('Test vs Pred MSE:', round(mean_squared_error(y_test, yhat), 2))
print('Test vs Pred RMSE:', round(np.sqrt(mean_squared_error(y_test, yhat)), 2))
print('CBR, R-Squared:', round(linear_score * 100, 2),'%')

      Actual  Predicted
0  431000.00  363463.16
1  240000.00  284051.12
2  367500.00  412286.03
3  374950.00  371274.63
4  500000.00  472254.99
..       ...        ...
15 406000.00  469539.71
16 750000.00  765825.73
17 360000.00  361275.66
18 357500.00  355462.56
19 622500.00  548651.31

[20 rows x 2 columns]

Test vs Pred MAE: 40742.03
Test vs Pred MSE: 3321013142.04
Test vs Pred RMSE: 57628.23
CBR, R-Squared: 89.9 %


## Fit the CatBoostRegressor model
import matplotlib.ticker as ticker
model7.fit(X_train, y_train, verbose=False)

y_pred = model7.predict(X_test) # make prediction of price
residuals = y_test - y_pred # calculate the residual

## residual chart
plt.figure(figsize=(15,10))
plt.scatter(y_pred, residuals, c='orange', alpha=0.8)
plt.axhline(0, color='blue', linestyle='--')
plt.xlabel('Predictions')
plt.xticks(range(100000, 1000001, 50000), rotation=45)

plt.ylabel('Residuals')
plt.title('Residual Plot - CatBoostRegressor')


plt.show()


## Residual distribution in bokeh library

data = pd.DataFrame({'Predictions': y_pred, 'Residuals': residuals})# create dataframe
plt_r =data.groupby(['Residuals'])['Residuals'].mean() # plot data

##  Assogn RGB color
red = 200
green = 50
blue = 100
color = RGB(r = red,
            g = green,
            b = blue)

plt_r.plot_bokeh(kind="hist", title="Mean residual distribution plot",
                 figsize=(800, 500),
                 xlabel="Residuals",
                 ylabel="Frequency",
                 stacked=True,
                color = color)

KC HOUSE SALE DATA REGRESSION ANALYSIS¶

EXPLORATORY ANALYSIS¶

DATA VISUALIZATION¶

DATA PREPROCESSING¶

BUILDING REGRESSION MODELS¶

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	condition	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
94	6021501535	20141223T000000	700000.0	3	1.50	1580	5000	1.0	0	0	3	8	1290	290	1939	0	98117	47.6870	-122.386	1570	4500
314	4139480200	20141209T000000	1400000.0	4	3.25	4290	12103	1.0	0	3	3	11	2690	1600	1997	0	98006	47.5503	-122.102	3860	11244
325	7520000520	20150311T000000	240500.0	2	1.00	1240	12092	1.0	0	0	3	6	960	280	1922	1984	98146	47.4957	-122.352	1820	7460
346	3969300030	20141229T000000	239900.0	4	1.00	1000	7134	1.0	0	0	3	6	1000	0	1943	0	98178	47.4897	-122.240	1020	7138
372	2231500030	20150324T000000	530000.0	4	2.25	2180	10754	1.0	0	0	5	7	1100	1080	1954	0	98133	47.7711	-122.341	1810	6929
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
20181	7853400250	20150219T000000	645000.0	4	3.50	2910	5260	2.0	0	0	3	9	2910	0	2012	0	98065	47.5168	-121.883	2910	5260
20613	2724049222	20141201T000000	220000.0	2	2.50	1000	1092	2.0	0	0	3	7	990	10	2004	0	98118	47.5419	-122.271	1330	1466
20670	8564860270	20150330T000000	502000.0	4	2.50	2680	5539	2.0	0	0	3	8	2680	0	2013	0	98045	47.4759	-121.734	2680	5992
20780	6300000226	20150504T000000	380000.0	4	1.00	1200	2171	1.5	0	0	3	7	1200	0	1933	0	98133	47.7076	-122.342	1130	1598
21581	7853420110	20150504T000000	625000.0	3	3.00	2780	6000	2.0	0	0	3	9	2780	0	2013	0	98065	47.5184	-121.886	2850	6000

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	condition	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
0	7129300520	20141013T000000	221900.0	3	1.00	1180	5650	1.0	3	7	1180	0	1955	0	98178	47.5112	-122.257	1340	5650
1	6414100192	20141209T000000	538000.0	3	2.25	2570	7242	2.0	3	7	2170	400	1951	1991	98125	47.7210	-122.319	1690	7639
2	5631500400	20150225T000000	180000.0	2	1.00	770	10000	1.0	3	6	770	0	1933	0	98028	47.7379	-122.233	2720	8062
3	2487200875	20141209T000000	604000.0	4	3.00	1960	5000	1.0	5	7	1050	910	1965	0	98136	47.5208	-122.393	1360	5000
4	1954400510	20150218T000000	510000.0	3	2.00	1680	8080	1.0	3	8	1680	0	1987	0	98074	47.6168	-122.045	1800	7503

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	condition	grade	sqft_above	yr_built	zipcode	lat	long	sqft_living15	sqft_lot15
21608	263000018	20140521T000000	360000.0	3	2.50	1530	1131	3.0	3	8	1530	2009	98103	47.6993	-122.346	1530	1509
21609	6600060120	20150223T000000	400000.0	4	2.50	2310	5813	2.0	3	8	2310	2014	98146	47.5107	-122.362	1830	7200
21610	1523300141	20140623T000000	402101.0	2	0.75	1020	1350	2.0	3	7	1020	2009	98144	47.5944	-122.299	1020	2007
21611	291310100	20150116T000000	400000.0	3	2.50	1600	2388	2.0	3	8	1600	2004	98027	47.5345	-122.069	1410	1287
21612	1523300157	20141015T000000	325000.0	2	0.75	1020	1076	2.0	3	7	1020	2008	98144	47.5941	-122.299	1020	1357

	id	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	condition	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
count	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00	21436.00
mean	4580765328.18	540529.29	3.37	2.12	2082.70	15135.64	1.50	0.01	0.24	3.41	7.66	1790.96	291.74	1971.10	84.73	98077.86	47.56	-122.21	1988.31	12785.96
std	2876589633.67	367689.30	0.93	0.77	919.15	41538.62	0.54	0.09	0.77	0.65	1.17	829.03	442.78	29.39	402.43	53.47	0.14	0.14	685.70	27375.47
min	1000102.00	75000.00	0.00	0.00	290.00	520.00	1.00	0.00	0.00	1.00	1.00	290.00	0.00	1900.00	0.00	98001.00	47.16	-122.52	399.00	651.00
25%	2123700078.75	322150.00	3.00	1.75	1430.00	5040.00	1.00	0.00	0.00	3.00	7.00	1200.00	0.00	1952.00	0.00	98033.00	47.47	-122.33	1490.00	5100.00
50%	3904921185.00	450000.00	3.00	2.25	1920.00	7614.00	1.50	0.00	0.00	3.00	7.00	1560.00	0.00	1975.00	0.00	98065.00	47.57	-122.23	1840.00	7620.00
75%	7308675062.50	645000.00	4.00	2.50	2550.00	10696.25	2.00	0.00	0.00	4.00	8.00	2220.00	560.00	1997.00	0.00	98117.00	47.68	-122.12	2370.00	10087.25
max	9900000190.00	7700000.00	33.00	8.00	13540.00	1651359.00	3.50	1.00	4.00	5.00	13.00	9410.00	4820.00	2015.00	2015.00	98199.00	47.78	-121.31	6210.00	871200.00

	id	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	condition	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
id	1.00	-0.02	0.00	0.00	-0.01	-0.13	0.02	-0.00	0.01	-0.02	0.01	-0.01	-0.01	0.02	-0.02	-0.01	-0.00	0.02	-0.00	-0.14
price	-0.02	1.00	0.31	0.53	0.70	0.09	0.26	0.27	0.40	0.04	0.67	0.61	0.32	0.05	0.13	-0.05	0.31	0.02	0.58	0.08
bedrooms	0.00	0.31	1.00	0.52	0.58	0.03	0.18	-0.01	0.08	0.03	0.36	0.48	0.30	0.15	0.02	-0.15	-0.01	0.13	0.39	0.03
bathrooms	0.00	0.53	0.52	1.00	0.75	0.09	0.50	0.06	0.19	-0.13	0.66	0.68	0.28	0.51	0.05	-0.20	0.02	0.22	0.57	0.09
sqft_living	-0.01	0.70	0.58	0.75	1.00	0.17	0.35	0.10	0.28	-0.06	0.76	0.88	0.43	0.32	0.06	-0.20	0.05	0.24	0.76	0.18
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
zipcode	-0.01	-0.05	-0.15	-0.20	-0.20	-0.13	-0.06	0.03	0.09	0.00	-0.18	-0.26	0.08	-0.35	0.06	1.00	0.27	-0.56	-0.28	-0.15
lat	-0.00	0.31	-0.01	0.02	0.05	-0.09	0.05	-0.01	0.01	-0.02	0.11	-0.00	0.11	-0.15	0.03	0.27	1.00	-0.14	0.05	-0.09
long	0.02	0.02	0.13	0.22	0.24	0.23	0.12	-0.04	-0.08	-0.11	0.20	0.34	-0.15	0.41	-0.07	-0.56	-0.14	1.00	0.33	0.25
sqft_living15	-0.00	0.58	0.39	0.57	0.76	0.14	0.28	0.09	0.28	-0.10	0.71	0.73	0.20	0.32	-0.00	-0.28	0.05	0.33	1.00	0.18
sqft_lot15	-0.14	0.08	0.03	0.09	0.18	0.72	-0.01	0.03	0.07	-0.00	0.12	0.19	0.02	0.07	0.01	-0.15	-0.09	0.25	0.18	1.00

	bedrooms	bathrooms	sqft_living	sqft_lot	floors	condition	grade	sqft_above	sqft_basement	yr_built	zipcode	lat	long	sqft_living15	sqft_lot15
0	-0.34	-1.51	-1.03	-0.40	-0.86	-0.65	-0.48	-0.68	-0.65	-0.58	1.84	-0.34	-0.22	-0.90	-0.41
2	-1.63	-1.51	-1.70	0.90	-0.86	-0.65	-1.72	-1.36	-0.65	-1.33	-0.97	1.26	-0.03	1.81	0.40
3	0.95	1.59	0.23	-0.59	-0.86	2.44	-0.48	-0.89	1.93	-0.23	1.05	-0.27	-1.32	-0.86	-0.62
4	-0.34	0.04	-0.22	0.33	-0.86	-0.65	0.77	0.15	-0.65	0.52	-0.11	0.41	1.49	0.00	0.21
6	-0.34	0.43	-0.16	-0.05	0.96	-0.65	-0.48	0.21	-0.65	0.79	-1.43	-1.75	-0.78	0.86	-0.01

	Feature	Score
6	grade	5740.69
2	sqft_living	5359.63
11	lat	4669.37
13	sqft_living15	3736.32
7	sqft_above	2987.01
...	...	...
5	condition	106.25
3	sqft_lot	105.43
12	long	35.51
9	yr_built	2.23
10	zipcode	1.23

	importance
feature
lat	51.00
sqft_living	13.36
long	7.51
grade	6.63
zipcode	4.59
...	...
sqft_lot15	1.13
sqft_basement	0.95
bathrooms	0.51
bedrooms	0.39
floors	0.12

	importance
feature
lat	52.12
sqft_living	12.61
long	7.29
grade	6.93
zipcode	4.81
...	...
sqft_above	2.70
condition	1.91
sqft_lot	1.72
sqft_lot15	1.55
sqft_basement	0.77

	Model	RMSE
Score
0.90	CBR	58215.37
0.89	GBR	59090.50
0.89	XGB	59727.61
0.88	Random Forest	62901.19
0.79	KNN	83653.87
0.76	TREE	88687.56